Database & Data Management · head to head
ClickHouse vs Mode

ClickHouse
Database & Data Management
Fast open-source column-oriented database for real-time analytics
- From
- Free
- Rated
- -
The short version
- Each has a real cost: ClickHouse limited multi-row atomic transactions and expensive UPDATE/DELETE operations unsuitable for transactional systems; Mode free tier limited to 4GB RAM and 1 CPU for SQL notebooks, insufficient for large datasets
- They diverge on capability: ClickHouse covers Column-oriented Storage, Mode covers SQL Editor.
Where they differ
Only the attributes on which ClickHouse and Mode actually diverge.
| Attribute | ClickHouse | Mode |
|---|---|---|
| Pricing model | Unknown | subscription |
| Platforms | Linux, macOS, Windows (via Docker) | Web |
| Category | Database & Data Management | Business Intelligence |
| Founded | 2021 | 2013 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in ClickHouse
- Column-oriented Storage
- Real-time Analytics
- SQL Support
- Linear Scalability
- Data Compression
- Vectorized Query Execution
- Approximate Calculations
- Kafka
Only in Mode
- SQL Editor
- Python/R Notebooks
- Interactive Reports
- Version Control
- Scheduling
- Snowflake
- Redshift
- BigQuery
Both cover
- PostgreSQL
- Web support
What people use each for
The jobs each tool is most often brought in to do.
ClickHouse
- Business intelligencenot Mode
- Data warehousingnot Mode
- Real-time analyticsnot Mode
- Reportingnot Mode
- Machine learningnot Mode
Mode
- Self-service analyticsnot ClickHouse
- Data explorationnot ClickHouse
- Ad-hoc reportingnot ClickHouse
- Collaborative analysisnot ClickHouse
- Embedded analyticsnot ClickHouse
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
ClickHouse
- Limited multi-row atomic transactions and expensive UPDATE/DELETE operations unsuitable for transactional systems
- Requires upfront schema design discipline with MergeTree engine choices and sort/partition keys
- Experimental vector search support, not production-ready for vector operations
- Different query syntax from standard SQL requiring migration planning
- Limited JOIN capabilities compared to traditional relational databases
- Migration complexity with 2-4 weeks estimated for data type mapping and query translation
Mode
- Free tier limited to 4GB RAM and 1 CPU for SQL notebooks, insufficient for large datasets
- Requires SQL knowledge for most analysis tasks, creating dependency on technical resources
- Paid plan pricing not publicly listed; requires sales consultation
- Recently acquired by ThoughtSpot in 2026, creating product direction uncertainty
- Limited customization options for visual aspects and embedded analytics
Pricing, plan by plan
ClickHouse
FreeNo published plan breakdown. See the ClickHouse review.
Mode
Free- FreeFree
- SQL Editor
- Python/R Notebooks
- Basic Charts
- Business$65/month
- Advanced Visualizations
- Collaboration
- Integrations
Which should you pick?
Choose ClickHouse if
- You need column-oriented storage.
- You want to start without paying.
- You work on Linux, macOS, Windows (via Docker).
- You also want real-time analytics.
Choose Mode if
- You need sql editor.
- You want to start without paying.
- You also want python/r notebooks.
Questions people ask
- Is ClickHouse or Mode better?
- Neither clearly leads. ClickHouse starts at Free and Mode at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, ClickHouse or Mode?
- ClickHouse starts at Free and Mode at Free.
- Does ClickHouse or Mode run on more platforms?
- ClickHouse runs on Linux, macOS, Windows (via Docker). Mode runs on Web.
- Can I use ClickHouse for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is ClickHouse best used for?
- ClickHouse is most often used for business intelligence, data warehousing, real-time analytics, reporting. Of those, business intelligence and data warehousing are not what Mode is typically brought in for.
- What can ClickHouse do that Mode cannot?
- ClickHouse covers Column-oriented Storage, Real-time Analytics, SQL Support, Linear Scalability. Mode covers SQL Editor, Python/R Notebooks, Interactive Reports, Version Control. Both handle PostgreSQL, Web support.
Answered from the vendors’ own pages
ClickHouse: What is ClickHouse best used for?
ClickHouse is optimized for analytical workloads on large datasets. It excels at fast aggregations and queries, being 10-100x faster than PostgreSQL on large aggregations.
SourceMode: What languages does Mode support for analysis?
Mode notebooks support SQL, Python (3.11 with pandas, NumPy, scikit-learn, matplotlib), and R (4.2.0 with ggplot2, dplyr, tidyr). Both Python and R allow additional library installation at runtime.
SourceClickHouse: Does ClickHouse support transactions?
ClickHouse has limited transaction support and expensive UPDATE/DELETE operations. It is not suitable for transactional workloads requiring strict ACID guarantees.
SourceMode: Can I integrate Mode notebook results into reports?
Yes. Mode allows adding notebook cell results directly to reports, with synchronized scheduling so reports re-run to keep data current.
SourceClickHouse: How does ClickHouse compare to PostgreSQL?
ClickHouse is 10-100x faster for analytics but PostgreSQL is better for transactional workloads. Many teams use both: PostgreSQL for writes via MaterializedPostgreSQL replication to ClickHouse for analytics.
SourceMode: Does Mode support collaborative analysis?
Yes. Mode notebooks provide moveable code blocks and markdown cells enabling exploratory analysis and team collaboration on data queries and visualizations.
SourceRelated pages
Other head to heads
- ClickHouse vs Cockroach Labs
- ClickHouse vs PostgreSQL
- ClickHouse vs Airtable
- ClickHouse vs Amazon Aurora
- ClickHouse vs Elasticsearch
- ClickHouse vs PlanetScale
- ClickHouse vs Azure SQL
- ClickHouse vs Couchbase
- ClickHouse vs DuckDB
- ClickHouse vs DynamoDB
- ClickHouse vs MariaDB
- ClickHouse vs Oracle Database
- ClickHouse vs Amazon RDS
- ClickHouse vs Amazon Redshift
- ClickHouse vs Apache Druid
- ClickHouse vs Cassandra
- ClickHouse vs CouchDB
- ClickHouse vs Firebolt
- ClickHouse vs Amazon QuickSight
- ClickHouse vs Power BI
- ClickHouse vs Sisense
- ClickHouse vs MicroStrategy
- ClickHouse vs Qlik Sense
- ClickHouse vs Domo
- ClickHouse vs GoodData
- ClickHouse vs ThoughtSpot
- ClickHouse vs Google Data Studio
- ClickHouse vs IBM Cognos Analytics
- ClickHouse vs Periscope Data
- ClickHouse vs Baremetrics
- ClickHouse vs Celonis
- ClickHouse vs Chartio
- ClickHouse vs ChartMogul
- ClickHouse vs Cyfe
- ClickHouse vs Databox
- ClickHouse vs Dundas BI
- Mode vs Cockroach Labs
- Mode vs PostgreSQL
- Mode vs Airtable
- Mode vs Amazon Aurora
- Mode vs Elasticsearch
- Mode vs PlanetScale
- Mode vs Azure SQL
- Mode vs Couchbase
- Mode vs DuckDB
- Mode vs DynamoDB
- Mode vs MariaDB
- Mode vs Oracle Database
- Mode vs Amazon RDS
- Mode vs Amazon Redshift
- Mode vs Apache Druid
- Mode vs Cassandra
- Mode vs CouchDB
- Mode vs Firebolt
- Mode vs Amazon QuickSight
- Mode vs Power BI
- Mode vs Sisense
- Mode vs MicroStrategy
- Mode vs Qlik Sense
- Mode vs Domo
- Mode vs GoodData
- Mode vs ThoughtSpot
- Mode vs Google Data Studio
- Mode vs IBM Cognos Analytics
- Mode vs Periscope Data
- Mode vs Baremetrics
- Mode vs Celonis
- Mode vs Chartio
- Mode vs ChartMogul
- Mode vs Cyfe
- Mode vs Databox
- Mode vs Dundas BI

